Traditional monitoring techniques for a large area using mobile sensors are based on periodically checking on different sub-areas of the large area. This guarantees a maximum age of the observation, but is inappropriate for most scenarios, where the rate of change for the observed variable can greatly differ from location to location and change over time.
There are numerous scenarios where surveillance and monitoring of large areas are required. These may include:                Monitoring and surveillance of large areas for agriculture purposes        Environmental or other survey operations such as deforestation, air pollution or in the context of traffic analysis        Monitoring due to contractually agreed security contracts and/or risk assessment to prevent danger escalation at crowded places, e.g. department stores, hospitals, airports, schools, etc.        
Typically, the areas to be observed are orders of magnitude larger than the sum of the areas each individual sensing platform can observe at any given time, in particular if the number of devices of a fleet is fixed. This leads to the need to coordinate and schedule the sensing platforms in a semi-coordinated fashion.
The basic rule of thumb normally involves ensuring that the last observation for each sub area is fresher than a threshold maximum age. Naturally, this leads to inefficiencies, since not all areas exhibit the same risks or change dynamics. To palliate this, it is common to use domain knowledge and manual adjustments to ensure that critical areas are observed more regularly or even continuously.
The following application scenario applying a known method or system illustrates its problems: For the purpose of traffic analysis one might decide to have a fleet of drones with zenithal cameras looking down on the streets to estimate traffic and people counts at different parts of a city. It would make sense to ensure that more drones oversee the downtown areas on weekdays, or that at least drones either stay longer in the area or come more regularly.